config.yml.example

MODE: 1             # 1: train, 2: test, 3: eval
MODEL: 1            # 1: edge model, 2: SR model, 3: joint SR model with edge enhancer
SCALE: 4            # scale factor (2, 4, 8)
SEED: 10            # random seed
GPU: [0]            # list of gpu ids
DEBUG: 0            # turns on debugging mode
VERBOSE: 0          # turns on verbose mode in the output console

TRAIN_FLIST_LR: ./datasets/places2_train.flist
TRAIN_FLIST_HR: ./datasets/places2_train.flist

VAL_FLIST_LR: ./datasets/places2_val.flist
VAL_FLIST_HR: ./datasets/places2_val.flist

TEST_FLIST_LR: ./datasets/places2_test.flist

LR: 0.0001                    # learning rate
BETA1: 0.0                    # adam optimizer beta1
BETA2: 0.9                    # adam optimizer beta2
BATCH_SIZE: 8                 # input batch size for training
HR_SIZE: 256                  # HR image size for training 0 for original size
SIGMA: 2                      # standard deviation of the Gaussian filter used in Canny edge detector
MAX_ITERS: 2e7                # maximum number of iterations to train the model
EDGE_THRESHOLD: 0.5           # edge detection threshold

L1_LOSS_WEIGHT: 1             # l1 loss weight
FM_LOSS_WEIGHT: 10            # feature-matching loss weight
STYLE_LOSS_WEIGHT: 250        # style loss weight
CONTENT_LOSS_WEIGHT: 0.1      # content loss weight
ADV_LOSS_WEIGHT1: 1           # edge model adversarial loss weight
ADV_LOSS_WEIGHT2: 1           # SR model adversarial loss weight
GAN_LOSS: hinge               # nsgan | lsgan | hinge

SAVE_INTERVAL: 1000           # how many iterations to wait before saving model (0: never)
SAMPLE_INTERVAL: 1000         # how many iterations to wait before sampling (0: never)
SAMPLE_SIZE: 12               # number of images to sample
EVAL_INTERVAL: 0              # how many iterations to wait before model evaluation (0: never)
LOG_INTERVAL: 10              # how many iterations to wait before logging training status (0: never)